RDF2vec [1]} is a representation learning approach for entities in a knowledge graph. The basic idea is to first create sequences from a knowledge graph by starting random walks from each node. These sequences are then fed into the word2vec algorithm [2]} for creating word embeddings, with each entity or property in the graph being treated as a “word”. As a result, a fixed-size feature vector is obtained for each entity.
